For my data set, I am utilizing SPSS as a statistical analysis tool. Regarding the kurtosis idea and the data produced by SPSS and Excel, I have a few questions. Please clarify the following understandings and ask further questions:
-
Kurtosis is a measure of flatness or peakness (hump) around the mean in the distribution. In terms of distribution tails, it tells whether the dataset is heavy-tailed or light-tailed relative to a normal distribution.
-
A normal distribution has a kurtosis of exactly 3 (excess kurtosis of exactly 0 which is Kurt-3) and is also called a mesokurtic distribution. A distribution with high kurtosis will have its peak bigger than the mesokurtic peak and is called as leptokurtic A distribution with low kurtosis will have its peak smaller than a mesokurtic peak and is called as platykurtic.
Questions:
-
What does it mean by excess kurtosis and what is the significance of using it? I am not getting a clear picture between kurtosis and excess kurtosis except that excess kurtosis is kurtosis-3 so we take 0 as a baseline.
-
SPSS tool generates "excess kurtosis" values or simple "kurtosis" values? In other words what baseline do we generally consider in SPSS for kurtosis measurement and inference? Is it 0 or 3? In SPSS I am getting a kurtosis of 1.16. So if I consider 3 as the baseline then 1.16 is less than 3 and so my distribution could be platykurtic. But if I consider the baseline as 0 (excess kurtosis), then 1.16 is clearly greater than 0 and so my distribution could be leptokurtic.
-
How it works out in excel again? Does the excel formula internally compute kurtosis as (Kurt - 3) or simple Kurt? I mean how to infer the result in MS excel too (baseline 3 or 0)?
